Use DSE Analytics to analyze huge databases. DSE Analytics includes built-in integration with Apache Spark and the DSEFS
distributed file system for storing large amounts of data for analytic processing.

Use DSE Analytics to analyze huge databases. DSE Analytics includes built-in integration with Apache Spark and the DSEFS
distributed file system for storing large amounts of data for analytic processing.

DSE Analytics

Use DSE Analytics to analyze huge databases. DSE Analytics includes built-in
integration with Apache Spark and the DSEFS distributed file system for storing large amounts of
data for analytic processing.

DSE Analytics features

No single point of failure

DSE Analytics supports a peer-to-peer, distributed cluster for running Spark
jobs. Being peers, any node in the cluster can load data files, and any
analytics node can assume the responsibilities of Spark Master.

Spark Master management

DSE Analytics provides automatic Spark Master management.

Analytics without ETL

Using DSE Analytics, you run Spark jobs directly against data in the
database. You can perform real-time and analytics workloads at the same time
without one workload affecting the performance of the other. Starting some
cluster nodes as Analytics nodes and others as pure transactional real-time
nodes automatically replicates data between nodes.

DSEFS (DataStax Enterprise file system) is a fault-tolerant,
general-purpose, distributed file system within DataStax Enterprise. It is
designed for use cases that need to leverage a distributed file system for
data ingestion, data staging, and state management for Spark Streaming
applications (such as checkpointing or write-ahead logging). DSEFS is
similar to HDFS, but avoids the deployment complexity and single point of
failure typical of HDFS. DSEFS is HDFS-compatible and is designed to work in
place of HDFS in Spark and other systems.